Category Archives: Artificial Intelligence

Artificial Intelligence | Azure Blog and Updates | Microsoft …

Thursday, January 16, 2020

Today, advancing our commitment to enhancing accessibility through technology, were announcing the general availability of Immersive Reader, an Azure Cognitive Service.

Tuesday, December 3, 2019

Conversational artificial intelligence (AI) is enabling organizations to improve their business in areas like customer service and employee engagement by automating some of the most commonly requested services, which frees up employees to take on more value-adding activities. While the benefits of conversational AI are well established, determining who in an organization should build these solutions is not always clear.

Tuesday, November 26, 2019

In an age where low-latency and data security can be the lifeblood of a business, containers make it possible for enterprises to meet these needs when harnessing artificial intelligence (AI). Since introducing Azure Cognitive Services in containers this time last year, businesses across industries have unlocked new productivity gains and insights.

Monday, November 25, 2019

Multi-language speech transcription was recently introduced into Microsoft Video Indexer at the International Broadcasters Conference (IBC). It is available as a preview capability and customers can already start experiencing it in our portal.

Wednesday, November 20, 2019

Organizations face challenges when it comes to extracting insights, finding meaning, and uncovering new opportunities in the vast troves of content at their disposal. In fact, 82 percent of

Monday, November 18, 2019

It's exciting to see the PyTorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced.

Thursday, November 7, 2019

This week at Microsoft Ignite, we announced updates to our products to make it easier for organizations to build robust conversational solutions, and to deploy them wherever their customers are. We are sharing some of the highlights below.

Wednesday, November 6, 2019

Azure Cognitive Services brings AI within reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, understand, and accelerate decision-making into your apps.

Monday, November 4, 2019

Over the past few years, weve seen incredible transformation across industries as companies harness the power of AI to transform business processes and drive impact for their customers.

Tuesday, October 29, 2019

Congratulations to the TensorFlow community on the release of TensorFlow 2.0! In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model.

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Artificial Intelligence | Azure Blog and Updates | Microsoft ...

What Is Artificial Intelligence | Artificial Intelligence Wiki

What is Artificial Intelligence (AI)?

The emergence of artificial intelligence (AI) has played a key part in ushering in the Fourth Industrial Revolution. According to the World Economic Forum, it is disrupting almost every industry in every country.

We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. Klaus Schwab,Founder and Executive Chairman, World Economic Forum Geneva

Artificial intelligence is a conglomeration of concepts and technologies that means different things to different people self-driving cars, robots that impersonate humans, machine learning, and more and its applications are everywhere you look. The typical definition of AI looks something like this:

Source: KDNuggets

Jeremy Achin, CEO of DataRobot, defines AI more simply:

An AI is a computer system that is able to perform tasks that ordinarily require human intelligence. These artificial intelligence systems are powered by machine learning. Many of them are powered by machine learning, some of them are powered by specifically deep learning, some of them are powered by very boring things like just rules.

For a more in-depth explanation, watch Jeremys keynote on the subject from the Japan AI Experience.

Artificial intelligence systems are critical for companies that wish to extract value from data by automating and optimizing processes or producing actionable insights. Artificial intelligence systems powered by machine learning enable companies to leverage large amounts of available data to uncover insights and patterns that would be impossible for any one person to identify, enabling them to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more.

Harvard Business Review gives key insight into how important AI is in todays economic environment:

The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, healthcare, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

Companies that fail to adopt AI and machine learning technologies are fated to be left behind:

DataRobot was founded on the belief that emerging AI and machine learning technologies should be available to all enterprises, regardless of size and resources. Thats why we invented automated machine learning, which allows users of all skill levels to easily and rapidly build and deploy machine learningmodels.

DataRobot believes in the democratization of AI, and for that reason, we developed a platform to enable business users across organizations to gain actionable, practical insights that result in tangible business value. DataRobot makes the power of AI accessible to users throughout your business, helping your organization transform into an AI-driven enterprise.

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What Is Artificial Intelligence | Artificial Intelligence Wiki

How Artificial Intelligence Will Make Decisions In Tomorrows Wars – Forbes

A US-built drone piloted by artificial intelligence. (Photo by Cristina Young/U.S. Navy via Getty ... [+] Images)

Artificial intelligence isn't only a consumer and business-centric technology. Yes, companies use AI to automate various tasks, while consumers use AI to make their daily routines easier. But governmentsand in particular militariesalso have a massive interest in the speed and scale offered by AI. Nation states are already using artificial intelligence to monitor their own citizens, and as the UK's Ministry of Defence (MoD) revealed last week, they'll also be using AI to make decisions related to national security and warfare.

The MoD's Defence and Security Accelerator (DASA) has announced the initial injection of 4 million in funding for new projects and startups exploring how to use AI in the context of the British Navy. In particular, the DASA is looking to support AI- and machine learning-based technology that will "revolutionise the way warships make decisions and process thousands of strands of intelligence and data."

In this first wave of funding, the MoD will share 1 million between nine projects as part of DASAs Intelligent Ship The Next Generation competition. However, while the first developmental forays will be made in the context of the navy, the UK government intends any breakthroughs to form the basis of technology that will be used across the entire spectrum of British defensive and offensive capabilities.

"The astonishing pace at which global threats are evolving requires new approaches and fresh-thinking to the way we develop our ideas and technology," said UK Defence Minister James Heappey. "The funding will research pioneering projects into how A.I and automation can support our armed forces in their essential day-to-day work."

More specifically, the project will be looking at how four conceptsautomation, autonomy, machine learning, and AIcan be integrated into UK military systems and how they can be exploited to increase British responsiveness to potential and actual threats.

"This DASA competition has the potential to lead the transformation of our defence platforms, leading to a sea change in the relationships between AI and human teams," explains Julia Tagg, the technical lead at the MoD's Defence Science and Technology Laborator (Dstl). "This will ensure UK defence remains an effective, capable force for good in a rapidly changing technological landscape."

On the one hand, such an adaption is a necessary response to the ever-changing nature of inter-state conflict. Instead of open armed warfare between states and their manned armies, geopolitical rivalry is increasingly being fought out in terms of such phenomena as cyber-warfare, micro-aggressive standoffs, and trade wars. As Julia Tagg explains, this explosion of multiple smaller events requires defence forces to be much more aware of what's happening in the world around them.

"Crews are already facing information overload with thousands of sources of data, intelligence, and information," she says. "By harnessing automation, autonomy, machine learning and artificial intelligence with the real-life skill and experience of our men and women, we can revolutionise the way future fleets are put together and operate to keep the UK safe."

That said, the most interestingand worryingelement of the Intelligent Ship project is the focus on introducing AI-enabled "autonomy" to the UK's defence capabilities. As a number of reports from the likes ofthe Economist, MIT Technology Review and Foreign Affairs have argued, AI-powered systems potentially come with a number of serious weaknesses. Like any code-based system they're likely to contain bugs that can be attacked by enemies, while the existence of biases in data (as seen in the context of law and employment) indicate that algorithms may simply perpetuate the prejudices and mistakes of past human decision-making.

It's for such reasons that the increasing fondness of militaries for AI is concerning. Not only is the British government stepping up its investment in military AI, but the United States government earmarked $927 million for "Artificial Intelligence / Machine Learning investments to expand military advantage" in last year's budget. As for China, its government has reportedly invested "tens of billions of dollars" in AI capabilities, while Russia has recently outlined an ambitious general AI strategy for 2030. It's even developing 'robot soldiers,' according to some reports.

So besides being the future of everything else, AI is likely to be the future of warfare. It will increasingly process defence-related information, filter such data for the greatest threats, make defence decisions based on its programmed algorithms, and perhaps even direct combat robots. This will most likely make national militaries 'stronger' and more 'capable,' but it could come at the cost of innocent lives, and perhaps even the cost of escalation into open warfare. Because as the example of Stanislav Petrov in 1983 proves, automated defence systems can't always be trusted.

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How Artificial Intelligence Will Make Decisions In Tomorrows Wars - Forbes

The World Economic Forum Jumps On the Artificial Intelligence Bandwagon – Forbes

Sergey Tarasov - stock.adobe.com

Last Friday, the World Economic Forum (WEF) sent out a press announcement about an artificial intelligence (AI) toolkit for corporate boards. The release pointed to a section of their web site titled Empowering AI Leadership. For some reason, at this writing, there is no obvious link to the toolkit, but the press team was quick to provide the link. It is well laid out in linked we pages, and some well-produced pdfs are available for download. For purposes of this article, I have only looked at the overview and the ethics section, so here are my initial impressions.

As would be expected from an organization focused on a select few in the world, the AI toolkit is high level. Boards of directors have broad but shallow oversight over companies, so there is no need to focus on details. Still, it is wished that a bit more accuracy had been involved.

The description of AI is very nice. There are many definitions and, as Ive repeatedly pointed out, the meaning of AI and of machine learning (ML) continue to both be changing and to have different meanings to many. The problem in the setup is one that many people miss about ML. In the introductory module, the WEF claims The breakthrough came in recent years, when computer scientists adopted a practical way to build systems that can learn. They support that with a link to an article that gets it wrong. The breakthrough mentioned in the article, the level of accuracy in an ML system, is far more driven by a non-AI breakthrough than a specific ML model.

When we studied AI in the 1980s, deep learning was known and models existed. What we couldnt do is run them. Hardware and operating systems didnt support the needed algorithms and the data volumes that were required to train them. Cloud computing is the real AI breakthrough. The ability to link multiple processors and computers in an efficient and larger virtual machine is what has powered the last decades growth of AI.

I was also amused about with list of core AI techniques where deep learning and neural networks are listed at the same level as the learning methods used to train them. Im only amused, not upset, because boards dont need to know the difference to start, but its important to introduce them to the terms. I did glance at the glossary, and its a very nice set of high-level definitions of some of these so interested board members can get some clarification.

On a quick tangent, their definition of bias is well done, as only a few short sentences reference both the real world issue of bias and the danger of bias within an AI system.

Ethics are an important component (in theory) to the management of companies. The WEF points out at the beginning of that module that technology companies, professional associations, government agencies, NGOs and academic groups have already developed many AI codes of ethics and professional conduct. The statement reminds me of the saying that standards are so important that everyone wants one of their own. The module then goes on to discuss a few of the issue of the different standards.

Where I differ from the WEF should be no surprise. This section strongly minimized governmental regulation. Its all up to the brave and ethical company. As Zuckerbergs decision that Facebook will allow lies in political advertisements as long as it makes the firm and himself wealthier, it is clear that governments must be more active in setting guidelines on technical companies, both in at large and within the AI arena. Two years ago, I discussed how the FDA is looking at how to handle machine learning. Governments move slowly, but they move. Its clear that companies need to be more aware of the changing regulatory environment. Ethical companies should be involved in both helping governments set reasonable regulations, ones that protect consumers as well as companies, and should be preparing systems, in advance, to match where they think a proper regulatory environment will evolve.

The WEFs Davos meetings are, regardless of my own personal cynicism about them, where government and business leaders meet to discuss critical economic issues. Its great to see the WEF taking a strong look at AI and then presenting what looks like a very good, introductory, toolkit for boards of directors, but the need for strong ethical positions means that more is needed. It will be interesting to see how their positioning advances over the next couple of years. F

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The World Economic Forum Jumps On the Artificial Intelligence Bandwagon - Forbes

The Ethical Upside to Artificial Intelligence – War on the Rocks

According to some, artificial intelligence (AI) is thenew electricity. Like electricity, AI will transform every major industry and open new opportunities that were never possible. However, unlike electricity, the ethics surrounding the development and use of AI remain controversial, which is a significant element constraining AIs full potential.

The Defense Innovation Board (DIB) released a paper in October 2019 that recommends the ethical use of AI within the Defense Department. It described five principles of ethically used AI responsible, equitable, traceable, reliable, and governable. The paper also identifies measures the Joint Artificial Intelligence Center, Defense Agency Research Projects Agency (DARPA), and U.S. military branches are taking to study the ethical, moral, and legal implications of employing AI. While the paper primarily focused on the ethics surrounding the implementation and use of AI, it also argued that AI must have the ability to detect and avoid unintended harm. This article seeks to expand on that idea by exploring AIs ability to operate within the Defense Department using an ethical framework.

Designing an ethical framework a set of principles that guide ethical choice for AI, while difficult, offers a significant upside for the U.S. military. It can strengthen the militarys shared moral system, enhance ethical considerations, and increase the speed of decision-making in a manner that provides decision superiority over adversaries.

AI Is Limited without an Ethical Framework

Technology is increasing the complexity and speed of war. AI, the use of computers to perform tasks normally requiring human intelligence, can be a means of speeding decision-making. Yet, due to a fear of machines inability to consider ethics in decisions, organizations are limiting AIs scope to focus ondata-supported decision-making using AI to summarize data while keeping human judgment as the central processor. For example, leaders within the automotive industry received backlash for programming self-driving cars to make ethical judgments. Some professional driving organizations have demanded that these cars be banned from the roads for at least 50 years.

This backlash, while understandable, misses the substantial upside that AI can offer to ethical decision-making. AI reflectshuman inputand operates on human-designed algorithms that set parameters for the collection and correlation of data to facilitate machine learning. As a result, it is possible to build an ethical framework that reflects a decision-makers values. Of course, when the data that humans supply is biased, for example, AI can mimic its trainers bydiscriminating on gender and race. Biased algorithms, to be sure, are a drawback. However, bias can be mitigated by techniques such as counterfactual fairness, Google AIs recommended practices, and algorithms such as those provided by IBMs AI Fairness 360 toolkit. Moreover, AI processing power makes it essential for successfully navigating ethical dilemmas in a military setting, where complexity and time pressure often obscure underlying ethical tensions.

A significant obstacle to building an ethical framework for AI is a fundamental element of war the trade-off between human lives and other military objectives. While international humanitarian law provides a codification of actions, many of which have ethical implications, it does not answer all questions related to combat. It primarily focuses on defining combatants, the treatment of combatants and non-combatants, and acceptable weapons. International humanitarian law does not deal with questions concerning how many civilian deaths are acceptable for killing a high-valued target, or how many friendly lives are worth sacrificing to take control of a piece of territory. While, under international law, these are examples of military judgments, this remains an ethical decision for the military leader responsible.

Building ethical frameworks into AI will help the military comply with international humanitarian law and leverage new opportunities while predicting and preventing costly mistakes in four ways.

Four Military Benefits of an Ethical AI Framework

Designing an ethical framework for AI will benefit the military by forcing its leaders to reexamine existing ethical frameworks. In order to supply the benchmark data on which AI can learn, leaders will need to define, label, and score choice options in ethical dilemmas. In doing so they will have three primary theoretical frameworks to leverage for guidance: consequentialist, deontological, and virtue. While consequentialist ethical theories focus on the consequences of the decision (e.g., expected lives saved), deontological ethical theories are concerned with the compliance with a system of rules (refusing to lie based on personal beliefs and values despite the possible outcomes). Virtue ethical theories are concerned with instilling the right amount of a virtuous quality into a person (too little courage is cowardice; too much is rashness; the right amount is courage). A common issue cited as anobstacle to machine ethicsis the lack of agreement on which theory or combination of theories to follow leaders will have to overcome this obstacle. This introspection will help them better understand their ethical framework, clarify and strengthen the militarys shared moral system, andenhance human agency.

Second, AI can recommend decisions that consistently reflect a leaders preferred ethical decision-making process. Even in high-stakes situations, human decision-making is prone to influence from factors that have little or nothing to do with the underlying choice. Things like poor nutrition, fatigue, and stress all common in warfare can lead to biased and inconsistent decision-making. Other influences, such as acting in ones self-interest or extreme emotional responses, can also contribute tomilitary members making unethical decisions. AI, of course, does not become fatigued or emotional. The consistency of AI allows it to act as a moral adviser by providing decision-makers morally relevant data leaders can rely on as their judgment becomes impaired. Overall, this can increase the confidence of young decision-makers, a concern thecommander of U.S. Army Training and Doctrine Commandbrought up early last year.

Third, AI can help ensure that U.S. military leaders make the right ethical choice however they define that in high-pressure situations. Overwhelming the adversary is central to modern warfare. Simultaneous attacks anddeception operationsaim to confuse decision-makers to the point where they can no longer use good judgment. AI can process and correlate massive amounts of data to provide not only response options, but also probabilities that a given option will result in an ethically acceptable outcome. Collecting battlefield data, processing the information, and making an ethical decision is very difficult for humans in a wartime environment. Although the task would still be extremely difficult, AI can gather and process information more efficiently than humans. This would be valuable for the military. For example, AI that is receiving and correlating information from sensors across the entire operating area could estimate non-combatant casualties, the proportionality of an attack, or social reactions from observing populations.

Finally, AI can also extend the time allowed to make ethical decisions in warfare. For example, a central concern in modern military fire support is the ability to outrange the opponent, to be able to shoot without being shot. The race to extend the range of weapons to outpace adversaries continues to increase the time between launch and impact. Future warfare will see weapons that are launched and enter an area that is so heavily degraded and contested that the weapon will lose external communication with the decision-maker who chose to fire it. Nevertheless, as the weapon moves closer to the target, it could gain situational awareness on the target area and identify changes pertinent to the ethics of striking a target. If equipped with onboard AI operating with an ethical framework, the weapon could continuously collect, correlate, and assess the situation throughout its flight to meet the parameters of its programmed framework. If the weapon identified a change in civilian presence or other information altering the legitimacy of a target, the weapon could divert to a secondary target, locate a safe area to self-detonate, or deactivate its fuse. This concept could apply to any semi- or fully autonomous air, ground, maritime, or space assets. The U.S. military could not afford a weapon system deactivating or returning to base in future conflicts each time it loses communication with a human. If an AI-enabled weapon loses the ability to receive human input, for whatever reason, an ethical framework will allow the mission to continue in a manner that aligns the weapons actions with the intent of the operator.

Conclusion

Building an ethical framework for AI will help clarify and strengthen the militarys shared moral system. It will allow AI to act as a moral adviser and provide feedback as the judgment of decision-makers becomes impaired. Similarly, an ethical framework for AI will maximize the utility of its processing power to help ensure ethical decisions when human cognition is overwhelmed. Lastly, providing AI an ethical framework can extend the time available to make ethical decisions. Of course, AI is only as good as the data it is provided.

AI should not replace U.S. military leaders as ethical decision-makers. Instead, if correctly designed, AI should clarify and amplify the ethical frameworks that U.S. military leaders already bring to war. It should help leaders grapple with their own moral frameworks, and help bring those frameworks to bear by processing more data than any decision-maker could, in places where no decision-maker could go.

AI may create new programming challenges for the military, but not new ethical challenges. Grappling with the ethical implications of AI will help leaders better understand moral tradeoffs inherent in combat. This will unleash the full potential of AI, and allow it to increase the speed of U.S. decision-making to a rate that outpaces its adversaries.

Ray Reeves is a captain in the U.S. Air Force and a tactical air control party officer and joint terminal attack controller (JTAC) instructor and evaluator at the 13thAir Support Operations Squadron on Fort Carson, Colorado. He has multiple combat deployments and is a doctoral student at Indiana Wesleyan University, where he studies organizational leadership. The views expressed here are his alone and do not necessarily reflect those of the U.S. government or any part thereof. Linkedin.

Image: U.S. Marine Corps (Photo by Lance Cpl. Nathaniel Q. Hamilton)

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The Ethical Upside to Artificial Intelligence - War on the Rocks

Seizing Artificial Intelligence’s Opportunities in the 2020s – AiThority

Artificial Intelligence (AI) has made major progress in recent years. But even milestones like AlphaGo or the narrow AI used by big tech only scratch the surface of the seismic changes yet to come.

Modern AI holds the potential to upend entire profession while unleashing brand new industries in the process. Old assumptions will no longer hold, and new realities will dictate those who are swallowed by the tides of change from those able to anticipate and ride the AI wave headlong into a prosperous future.

Heres how businesses and employees can both leverage AI in the 2020s.

Like many emerging technologies, AI comes with a substantial learning curve. As a recent McKinsey report highlights, AI is a slow burn technology that requires a heavy upfront investment, with returns only ramping up well down the road.

Because of this slow burn, an AI front-runner and an AI laggard may initially appear to be on equal footing. The front-runner may even be a bit behind during early growing pains. But as the effects of AI adoption kick in, the gap between the two widens dramatically and exponentially. McKinseys models estimate that within around 10 years, the difference in cumulative net change in cash flow between front-runners and laggards could be as high as 145 percent.

The first lesson for any business hoping to seize new AI opportunities is to start making moves to do so right now.

Read More: How is Artificial Intelligence (AI) Changing the Future of Architecture?

Despite popular opinion, the coming AI wave will be mostly a net positive for employees. The World Economic Forum found that by 2022, AI and Machine Learning will have created over 130 million new jobs. Though impressive, these gains will not be distributed evenly.

Jobs characterized by unskilled and repetitive tasks face an uncertain future, while jobs in need of greater social and creative problem-solving will spike. According to McKinsey, the coming decade could see a 10 percent fall in the share of low digital skill jobs, with a corresponding rise in the share of jobs requiring high digital skill.

So how can employees successfully navigate the coming future of work? One place to start is to investigate the past. Nearly half a century ago, the first ATM was installed outside Barclays Bank in London. In 1967, the thought of bank tellers surviving the introduction of automated teller machines felt impossible. ATMs caught on like wildfire, cut into tellers hours, offered unbeatable flexibility and convenience, and should have all but wiped tellers out.

But, in fact, exactly the opposite happened. No longer having to handle simple deposits freed tellers up to engage with more complex and social facets of the business. They started advising customers on mortgages and loans, forging relationships and winning loyalty. Most remarkable of all, in the years following the ATMs introduction, the total number of tellers employed worldwide didnt fall off a cliff. In fact, it rose higher than ever.

Though AI could potentially threaten some types of jobs, many jobs will see rising demand. Increased reliance on automated systems for core business functions, frees up valuable employee time and enables them to focus on different areas to add even more value to the company.

As employees grow increasingly aware of the changing nature of work, they are also clamoring for avenues for development, aware that they need to hold a variety of skills to remain relevant in a dynamic job market. Companies will, therefore, need to provide employees with a wide range of experiences and the opportunity to continuously enhance their skillsets or suffer high turnover. This is already a vital issue to businesses with the cost of losing an employee equating to 90%-200% of their annual salary. This costs each large enterprise an estimated $400 million a year. If employees feel their role is too restrictive or that their organization is lagging, their likelihood of leaving will climb.

The only way to capture the full value of AI for business is to retain the highly skilled employees capable of wielding it. Departmental silos and rigid job descriptions will have no place in the AI future.

Read More: How Artificial Intelligence and Blockchain is Revolutionizing Mobile Industry in 2020?

For employees to maximize their chances of success in the face of rapid AI advancement, they must remain flexible and continuously acquire new skills. Both businesses and employees will need to realign their priorities in accordance with new realities. Workers will have to be open to novel ideas and perspectives, while employers will need to embrace the latest technological advancements.

Fortunately, the resources and avenues for ambitious employers to pursue continued growth for their employees are blossoming. Indeed, the very AI advancements prompting the need for accelerated career development paths are also powering technologies to maximize and optimize professional enrichment.

AI is truly unlocking an exciting new future of work. Smart algorithms now enable hyper-flexible workplaces to seamlessly shuffle and schedule employee travel, remote work, and mentorship opportunities. At the cutting edge, these technologies can even let employees divide their time between multiple departments across their organization. AI can also tailor training and reskilling programs to each employees unique goals and pace.

The rise of AI holds the promise of great change, but if properly managed, it can be a change for the better.

Read More: Predictions of AI AdTech in 2020

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Seizing Artificial Intelligence's Opportunities in the 2020s - AiThority

Being human in the age of Artificial Intelligence – Deccan Herald

After a while, everything is overhyped and underwhelming. Even Artificial Intelligence has not been able to escape the inevitable reduction that follows such excessive hype. AI is everything and everywhere now and most of us wont even blink if we are toldAI is poweringsomeonestoothbrush. (It probably is).

The phrase is undoubtedly being misused but is the technology too? One thing is certain, whether we like it or not, whether we understand it or not, for good or bad, AI is playing a huge part in our everyday life today huger than we imagine. AI is being employed in health, wellness and warfare; it is scrutinizing you, helping you take better photos, making music, books and even love. (No, really. The first fully robotic sex doll is being created even as you are reading this.)

However, there is a sore lack of understanding of what AI really is, how it is shaping our future and why it is likely to alter our very psyche sooner or later. There is misinformation galore, of course. Either media coverage of AI is exaggerated (as if androids will take over the world tomorrow) or too specific and technical, creating further confusion and fuelling sci-fi-inspired imaginations of computers smarter than human beings.

So what is AI? No, we are not talking dictionary definitions here those you can Google yourself. Neither are we promising to explain everything that will need a book. We are onlyhoping to give you aglimpse into theextraordinary promise and peril of this single transformative technology as Prof Stuart Russell, one of the worlds pre-eminent AI experts, puts it.

Prof Russell has spent decades on AI research and is the author of Artificial Intelligence: A Modern Approach, which is used as a textbook on AI in over 1,400 universities around the world.

Machine learning first

Otherexperts believe our understanding of artificial intelligence should begin with comprehending machine learning, the so-called sub-field of AI butone that actually encompasses pretty much everything that is happening in AI at present.

In its very simplest definition, machine learning is enabling machines to learn on their own. The advantages of thisare easy to see. After a while, you need not tell it what to do it is your workhorse. All you need is to provide it data and it will keep coming up with smarter ways of digesting that data, spotting patterns, creating opportunities in short doing your work better than you perhaps ever could. This is the point where you need to scratch the surface. Scratch and you will stare into a dissolving ethical conundrum about what machines might end up learning. Because, remember they do not (cannot) explain their thinking process. Not yet, at least. Precisely why, the professor has a cautionary take.

The concept of intelligence is central to who we are. After more than 2,000 years of self-examination, we have arrived at a characterization of intelligence that can be boiled down to this: Humans are intelligent to the extent that our actions can be expected to achieve our objectives. Intelligence in machines has been defined in the same way: Machines are intelligent to the extent that their actions can be expected to achieve their objectives.

Whose objectives?

The problem,writes the professor, is in this very definition of machine intelligence. We say that machines are intelligent to the extent that their actions can be expected to achieve their objectives, but we have no reliable way to make sure that their objectives are the same as our objectives. He believes what we should have done all along is to tweak this definition to: Machines are beneficial to the extent that their actions can be expected to achieve our objectives.

The difficulty here is of course that our objectives are in us all eight billion of us and not in the machines. Machines will be uncertain about our objectives; after all we are uncertain about them ourselves but this is a good thing; this is a feature, not a bug. Uncertainty about objectives implies that machines will necessarily defer to humans they will ask permission, they will accept correction and they will allow themselves to be switched off.

Spilling out of the lab

This might mean a complete rethinking and rebuilding of the AI superstructure. Perhaps something that indeed is inevitable if we do not want this big event in human history to be the last, says the prof wryly. As Kai-Fu Lee, another AI researcher, said in an interview a while ago, we are at a moment where the technology is spilling out of the lab and into the world. Time to strap up then!

(With inputs from Human Compatible: AI and the Problem of Control by Stuart Russell, published by Penguin, UK. Extracted with permission.)

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Being human in the age of Artificial Intelligence - Deccan Herald

Artificial Intelligence, The Real Estate Industry And The Realtor – PR Web

With its data-driven, human-powered approach, TruConnectRE.Com provides the realtor with advanced AI software to manage and track the leads provided.

FALLS CHURCH, Va. (PRWEB) January 20, 2020

The Artificial Intelligence revolution has begun. Although it is still in its infancy, AI is already facilitating self-driving cars and voice-activated home systems that set alarms and thermostats on their own. The technology may come off as intimidating to many in the real estate industry, but a more involved understanding of how AI and machine learning can make lives easier will cause all to approve of the technology. At this stage, it's not instantly evident how real estate is influenced by artificial intelligence. This includes buying, selling, and maintenance of the Real estate. Despite some challenges, AI promises to deliver excellent benefits to those who use it right.

The buzzword in almost every sector, AI was predicted to substitute the manpower by computers. It hasnt happened yet and presumably, wouldnt happen in the next decade either. However, there are immense advantages to the companies that are ahead in technology as compared to their competitors. Artificial intelligence has the potential to increase efficiency by a substantial amount and has become the latest rung in the helix of the ever-growing real estate industrys evolution.

Not a very long time ago, realtors used to go from door to door looking for potential clients. It was a grueling chore that had even the most efficient and organized agents cold calling for hours a day. With the excessive amounts of data, even the tiniest of human errors could set everything back. Luckily for realtors, those days are well behind. The biggest strength of AI systems lies in having access to tons of data and being able to find patterns in that data, generating insights and inferences while augmenting peoples ability to make decisions based on that data.

Artificial intelligence may seem to pose a threat to realtors jobs but truly, its not something that agents should be afraid of. As mentioned earlier, even with the advanced technology, theres still a long time before mass industry adoption of Artificial Intelligence. In the meantime, realtors should embrace this technology as a unicorn as it can drastically reduce their workload, help them sell more, eliminate the pitfalls of any and all human error and help them reach home buyers that they might not be able to otherwise. Agents can save the time they otherwise would have had to spend on all the calling and making a clientele and spend it directly on the latent buyer instead.

An example of one such Real Estate that has become a front-runner in implementing artificial intelligence is TruConnectRE.Com TruConnect Real Estate is deeply involved in the agents gain. It not only provides them with solid leads but also offers a turn-key solution to the problems theyre facing. With its data-driven, human-powered approach, TruConnectRE.Com provides the realtor with advanced AI software to manage and track the leads provided. The best part? TruConnectRE charges only a 25% referral fee subsequent to a closing! An effective lead generation system uses both, inbound and outbound marketing to engage buyers and take them towards conversion. To facilitate that, TruConnect also tailors and manages marketing material, establishing an all-in-one platform for the realtor. With its growing approach, TruConnect Real Estate does all the work for the realtor by generating thousands of real estate leads from around the world. And it doesnt end with that. They call the turnover, strain and ensure follow-ups until they find the ideal client for an agent to occupy. In doing so, they not only increase efficiency and cut down costs but also rid the agents of the tedious task of calling leads. This frees up time for the realtor to focus on the buyer, closing 2 to 5 times the deals they normally do. Agents are introduced to screened and effectuate clients and are left to form a relationship and seal the deal.

With all eyes on Artificial Intelligence, its perceptible that this technology will largely impact the future of the real estate. It will be disruptive and will cut off some practices, but it will also save a great deal of time and money when it comes to operational efficiency and client service. Its ability to collect and analyze from a huge inflow of data is sure to make agents more effective, brokers more strategic, and clients ultimately empowered to experience the buying and selling process with a tap on the screens of their phones.

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Artificial Intelligence, The Real Estate Industry And The Realtor - PR Web

Artificial Intelligence in Healthcare Market Advanced Technology and New Innovations by 2023 Welltok, Intel, Nvidia, Google – Media Releases – CSO…

Market Industry Reports (MIR) has announced the addition of the Global Artificial Intelligence in Healthcare Market Research Report 2019 The report focuses on global major leading industry players with information such as company profiles, product picture and specification.

Artificial intelligence assists the machines to perform any task without human interventions. It uses different algorithms and software that help the machine to inculcate perception and reasoning for various situations. AI is widely applicable in the healthcare sector for various purposes such as drug discovery and precision medicine. In addition, it is used to analyze patients medical data, to predict disease onset, and personalize treatment provided to the patient. The global AI market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.0% from 2017 to 2023.

The AI market is segmented based on offering, technology, application, end user, and geography. On the basis of offering, the market is divided into hardware, software, and services. By technology, it is classified into deep learning, querying method, natural language processing, and context aware processing. Applications covered in the study include robot-assisted surgery, virtual nursing assistant, administrative workflow assistance, fraud detection, dosage error reduction, clinical trial participant identifier, preliminary diagnosis, and others. According to end user, the market is categorized into healthcare provider, pharmaceutical & Biotechnology Company, patient, and payer geographically, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

Major Key Players of the Artificial Intelligence in Healthcare Market are:Welltok, Intel Corporation, Nvidia Corporation, Google, IBM Corporation, Microsoft Corporation, General Vision, Enlitic, Next IT Corporation, iCarbonX

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The market growth is driven by rise in adoption rate of AI systems and delete technological advancements in the AI field. In addition, the ability of these systems to improve patient outcomes, increase in adoption of precision medicine, and increase in need for coordination between healthcare workforce & patients are expected to fuel the market growth. However, lack of standard regulations and guidelines and reluctance among healthcare professionals to adopt AI-based technologies are expected to hinder the market growth.

Major Types of Artificial Intelligence in Healthcare Market covered are:HardwareSoftwareService

Major Applications of Artificial Intelligence in Healthcare Market covered are:Robot-assisted SurgeryVirtual Nursing AssistantAdministrative Workflow AssistanceFraud DetectionDosage Error ReductionClinical Trial Participant IdentifierPreliminary DiagnosisOthers

Research objectives:-

To study and analyze the global Artificial Intelligence in Healthcare consumption (value & volume) by key regions/countries, product type and application, history data. To understand the structure of the Artificial Intelligence in Healthcare market by identifying its various sub-segments. Focuses on the key global Artificial Intelligence in Healthcare manufacturers, to define, describe and analyze the sales volume, value, market share, market competitive landscape, SWOT analysis, and development plans in the next few years. To analyze the Artificial Intelligence in Healthcare with respect to individual growth trends, future prospects, and their contribution to the total market. To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

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Table of Content

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Artificial Intelligence in Healthcare Market Size2.2 Artificial Intelligence in Healthcare Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Artificial Intelligence in Healthcare Market Size by Manufacturers3.2 Artificial Intelligence in Healthcare Key Players Head office and Area Served3.3 Key Players Artificial Intelligence in Healthcare Product/Solution/Service3.4 Date of Enter into Artificial Intelligence in Healthcare Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Artificial Intelligence in Healthcare Sales by Product4.2 Global Artificial Intelligence in Healthcare Revenue by Product4.3 Artificial Intelligence in Healthcare Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Artificial Intelligence in Healthcare Breakdown Data by End User

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In the end, Artificial Intelligence in Healthcare industry report specifics the major regions, market scenarios with the product price, volume, supply, revenue, production, and market growth rate, demand, forecast and so on. This report also presents SWOT analysis, investment feasibility analysis, and investment return analysis.

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Artificial Intelligence in Healthcare Market Advanced Technology and New Innovations by 2023 Welltok, Intel, Nvidia, Google - Media Releases - CSO...

M’sian courts to go digital and adopt artificial intelligence initiatives – The Star Online

KUALA LUMPUR (Bernama): The country's courts are not only going digital but are also adopting artificial intelligence (AI) initiatives to ensure easy access to justice.

Minister in the Prime Minister's Department Datuk Liew Vui Keong said the government was already pursuing an array of AI initiatives in digitalising the courts.

This includes the introduction of e-bail and e-review that seek to reduce the need for lawyers and litigants to physically appear in court, saving time and costs for all parties, and digital voice to text court recording transcripts and digitally secured evidence.

"Through 2020, the government will continue to pursue and introduce additional AI initiatives to digitalise the courts and secure easy access to justice for all, he said in a statement here on Monday (Jan 20).

"The legal profession must embrace digitalisation, in which the Sabah and Sarawak Judiciary have (sic) led an exemplary path for legal practitioners across Malaysia to follow.

"I am delighted to hear the judiciarys support of the governments efforts to digitalise the courts through use of AI and information technology (IT). Support from the nations top judges was crucial. I therefore wish to record the governments sincerest appreciation for the tremendous support of the Chief Justice and Chief Judge of Sabah and Sarawak for these initiatives, he said.

These digital initiatives would not only facilitate easy access to justice by removing the necessity for the physical presence of parties in court but would also be environmentally friendly as they seek to reduce the usage of paper and carbon footprints incurred in travel, Liew said.- Bernama

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M'sian courts to go digital and adopt artificial intelligence initiatives - The Star Online